Global Payroll Resources

Improving Team Performance With Payroll Analytics

Ask a payroll manager how well his or her team is doing, and there’s every chance you’ll get the answer as a percentage. That’s because for many businesses, payment accuracy is the only real measure of the payroll department’s performance.

In reality, though, payment accuracy statistics are just the tip of the iceberg. Even with a 100% hit rate, there’s no guarantee that your team is performing anywhere near its potential.

Analytical tools can help you delve deeper into your data to measure the performance of your payroll team, both collectively and individually, and drive greater efficiency and effectiveness across the department.

The message is simple: Don’t settle for delivering 100% payment accuracy when you can deliver 100% accuracy and so much more.

Putting Performance Into Context

The primary reason businesses struggle to evaluate payroll performance is a simple lack of data or, rather, a lack of visibility into the data that exists within their global payroll function.

While traditional applications have always focused on the calculation of payroll and the management of payroll data, they typically fail to collect vital information from all the related touch-points in the pay cycle.

Specifically, this means information such as the number and type of payroll issues recorded by individuals, or data around the use of communication portals. Another example is the average time elapsed between each step of the workflow, and average “effort time” each step takes.

This kind of data enables you to put performance into context, providing a more complete, balanced, and accurate picture of payroll delivery. It allows you to spot trends or issues that affect multiple payrolls. It facilitates a strategic and proactive approach to problem resolution.

Yet with no visibility into this essential data, a standardized approach to performance measurement becomes almost unattainable, and inevitably, differences arise in how local teams define success.

In effect, in-country teams are left to pick the metrics that suit them, rendering cross-border comparisons impossible. All that can be compared are the headline figures—the number of final payment and statutory filing errors in each payment run.

These figures are easy to identify, as they’ll be reported either by the employees or government departments who have been incorrectly paid. But while this data is important, it breeds a culture of reactive fixes rather than proactive improvement.

Sophisticated data analytics and dashboards are capable of delivering real-time information from all touch-points in the pay cycle. Issues can be flagged and improvements made on an ongoing basis. That means you’re far more likely to spot a potential mistake before it happens rather than constantly worrying about when the next one will be made.

Culture of Constant Improvement or Blame?

Relying solely on post-payment data instead of real-time payroll information gives rise to a number of problems.

A traditional outsourced operation offers limited insight into the root cause of processing errors. A culture of assigning blame can sometimes develop between payroll provider and client, with conflicting, highly subjective views as to what went wrong and why.

As well as putting a strain on the relationship, this reactive approach to problem identification also places a significant burden on your payroll employees—with post-payment data having to be manually gathered in an ad-hoc fashion from multiple systems, then discussed in a defect log or issues log meeting.

Reporting and performance measurement ultimately become dependent on how good your individuals are at recording, reviewing, and explaining the issues encountered in these meetings—with no standardized best practices to follow.

This approach sits entirely at odds with the very principle of global payroll. This reactive approach only serves to maintain the autonomy of local-level teams to deal with issues as they see fit and to measure success in the absence of any real standards.

Benchmarking Performance

The implementation of tools to capture more data throughout the payment process provides the platform for improvements in team performance. Equally important is your ability to visualize, analyze, and interpret your newfound wealth of data.

It is vital to build context around your figures, starting with metrics such as global and national performance averages. A good place to begin is by benchmarking results against other business units in a particular country. Then you’ll be able to start identifying certain territories where your payroll teams are struggling most and focus attention and deploy resources as appropriate.

This can also be the catalyst for building a culture of continuous improvement. For instance, identify your five local teams with the lowest performance and re-measure every month. If remedial action is taken and proves to be effective, you’d hope to have a new five teams to focus on every time. And so the cycle should continue.

Monitoring Individual Team Members

If a team is only as strong as its weakest player, then monitoring the individual performances of your payroll personnel is crucial.

You’ll gain few clues from those generic post-payment accuracy stats, but by using sophisticated payroll analytics applications to draw data from multiple touch-points in the payroll cycle, you will be able to gain insight into the activity of individual users.

With an intuitive global payroll dashboard in place, you can identify and drill down to gain invaluable insights to address the five following items:

Where issues originated on the payroll run and who recorded them

Which users are following your communications model correctly

Whether individuals are collaborating effectively and productively

How many rejections were on the payroll (this may not necessarily be the user’s fault, but gaining the insight into the numbers enables you to investigate more closely)

Which users missed deadlines on the payroll calendar (this could indicate a user with too much work to do, or simply struggling with workload)

Like overall payroll statistics, many of these metrics can and should be benchmarked against the average level of performance globally, regionally, or by country.

Similarly, to ensure you’re measuring “improvement” rather than just “performance,” metrics should also be measured against previous time periods. A team or user with an 80% data output accuracy rate for an individual payroll run might not seem that impressive, but this might represent a 20% improvement on the previous run.

Conversely, a team or individual with an 87% data output accuracy rate might have fallen 10% since the previous run, so it’s important to be able to identify which of your teams or individuals are on an upward or downward curve.

All of this data provides managers with a valuable opportunity to pinpoint strong performers and leverage their knowledge to help improve weaker members of the team. Seek to understand how your high achievers are achieving their results, and aim to replicate their success across all your teams globally.

Where to Start With Payroll Analytics

The examples above illustrate how leading global payroll teams are using data to continuously improve performance. They represent how utilizing payroll analytics can make the working lives of payroll managers and their teams less stressful and far more rewarding.

There’s no doubt accurate and effective performance measurement starts with a level playing field. So, your first challenge is to create standardized global workflows that ensure all teams are working to the same, clearly defined best practices throughout the payroll process.

That goes far beyond the delivery of the final payroll run, with standardization required in the way users record, track, and manage the timeliness and completeness of all payroll tasks or payroll issues. Even the way users communicate throughout delivery should be defined and enforced as far as possible.

In defining your new best practices, it’s important to understand what you are trying to measure and where you may be able to obtain that information. You might need to create additional touch-points, increasing the amount of data your users provide during the workflow — but don’t shy away from implementing these additional steps.

After all, the insights you can gain from this extra data will ultimately help you monitor, measure, and consequently improve performance—with any extra time taken offset by the time you’ll save on ad hoc data collection, or dealing with escalations from a poorly managed process.